Method, device, medium, program product and equipment for detecting contamination of a probe lens

Abstract

Embodiments of the present application provide a kind of detection lens dirty detection method, device, medium, program product and equipment, the detection lens is installed in cleaning equipment, the method comprises: in response to the cleaning equipment is located in target area, control the detection lens emits detection signal to the detection area corresponding to the target area;Obtain the light reflection signal of each detection point in the detection area, and determine light intensity data from the light reflection signal;Based on the light intensity data, determine the first dirty evaluation index of the detection lens, and the first dirty evaluation index is used to characterize the dirty degree of the detection lens. The technical scheme provided by the embodiments of the present application can accurately detect the dirty degree of the detection lens in the cleaning equipment.

Description

Technical Field

[0001] This application relates to the field of cleaning equipment technology, and more specifically, to a method, apparatus, medium, program product, and equipment for detecting dirt in a detection lens. Background Technology

[0002] With the continuous development of automation and artificial intelligence technologies, various cleaning equipment, such as robotic vacuum cleaners and robotic mops, are widely used. During the cleaning process, these devices rely on various sensors to detect the surrounding environment and obtain data. Analyzing this data guides the cleaning equipment to complete the task. However, if the sensors are contaminated, the accuracy of the data will decrease, affecting the precision of the cleaning equipment's actions and reducing cleaning quality. Therefore, accurately detecting the degree of dirt on the sensors in cleaning equipment is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The embodiments of this application provide a method, apparatus, medium, program product and equipment for detecting dirt in a probe lens. Based on the technical solution provided in this application, the degree of dirt in the probe lens of a cleaning device can be accurately detected.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the present application, a method for detecting dirt in a detection lens is provided. The detection lens is installed in a cleaning device. The method includes: in response to the cleaning device being located in a target area, controlling the detection lens to emit a detection signal toward a detection area corresponding to the target area; acquiring light reflection signals at each detection point within the detection area and determining light intensity data from the light reflection signals; and determining a first dirt evaluation index for the detection lens based on the light intensity data, wherein the first dirt evaluation index is used to characterize the degree of dirt in the detection lens.

[0006] In some embodiments of this application, based on the foregoing scheme, determining the first dirt evaluation index of the detection lens based on the light intensity data includes: determining the target feature value of the detection lens in at least one evaluation dimension based on the light intensity data; and comparing the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

[0007] In some embodiments of this application, based on the aforementioned scheme, the light intensity data includes at least one set of light intensity values, wherein the distance between each detection point corresponding to each set of light intensity values ​​and the target area satisfies a first preset condition.

[0008] In some embodiments of this application, based on the foregoing scheme, determining the target feature value of the detection lens in at least one evaluation dimension based on the light intensity data includes: performing calculations on each of the at least one set of light intensity values ​​according to preset calculation rules to obtain the target feature value of the detection lens in at least one evaluation dimension.

[0009] In some embodiments of this application, based on the foregoing scheme, the preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation.

[0010] In some embodiments of this application, based on the foregoing scheme, the method further includes: acquiring light intensity data determined by the cleaning device during the execution of historical cleaning tasks, as reference light intensity data; and determining a reference feature value corresponding to the target feature value based on the reference light intensity data.

[0011] In some embodiments of this application, based on the foregoing scheme, the step of comparing the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens includes: for each target feature value, calculating the absolute value of the target difference between each target feature value and the corresponding reference feature value; calculating the ratio between each absolute value of the target difference and the corresponding reference feature value to obtain the first dirt evaluation index of the detection lens.

[0012] In some embodiments of this application, based on the foregoing scheme, the step of comparing the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens includes: calculating the ratio between each of the target feature values ​​and the corresponding reference feature value to obtain the first dirt evaluation index of the detection lens.

[0013] In some embodiments of this application, based on the foregoing scheme, the method further includes: obtaining a dirt assessment index of the detection lens when the cleaning device is located in other areas, as a second dirt assessment index; and determining whether to send a prompt message to the user based on the first dirt assessment index and / or the second dirt assessment index, the prompt message being used to prompt the user to clean the detection lens.

[0014] In some embodiments of this application, based on the aforementioned scheme, determining whether to send a prompt message to the user based on the first dirt assessment index and / or the second dirt assessment index includes: comparing the first dirt assessment index and / or the second dirt assessment index with a preset threshold to obtain a comparison result; if the comparison result meets a second preset condition, then sending a prompt message to the user.

[0015] In some embodiments of this application, based on the foregoing scheme, the method further includes: if the comparison result does not meet the second preset condition, then updating the reference feature value corresponding to the target feature value based on the light intensity data.

[0016] In some embodiments of this application, based on the foregoing scheme, after sending a prompt message to the user, the method further includes: in response to receiving confirmation information that the cleaning of the detection lens has been completed, controlling the cleaning device to re-determine the dirt evaluation index of the detection lens to obtain a new dirt evaluation index; if the new dirt evaluation index is within the range of a preset dirt evaluation index, then determining that the cleaning of the detection lens has been completed.

[0017] In some embodiments of this application, based on the foregoing scheme, after determining that the cleaning of the detection lens is complete, the method further includes: deleting the data recorded by the cleaning device that is associated with the reference feature value.

[0018] According to a second aspect of the present application, a dirt detection device for a detection lens is provided. The detection lens is installed on a cleaning device. The device includes: a control unit, configured to control the detection lens to emit a detection signal toward a detection area corresponding to the target area in response to the cleaning device being located in a target area; an acquisition unit, configured to acquire light reflection signals at each detection point within the detection area and determine light intensity data from the light reflection signals; and a determination unit, configured to determine a first dirt evaluation index for the detection lens based on the light intensity data, wherein the first dirt evaluation index characterizes the degree of dirtiness of the detection lens.

[0019] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores at least one piece of program code, the at least one piece of program code being loaded and executed by a processor to perform the operations performed by the method described in any of the first aspects above.

[0020] According to a fourth aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the method described in any of the first aspects above.

[0021] According to a fifth aspect of the embodiments of this application, a cleaning device is provided, the cleaning device being equipped with a detection lens, including one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to perform the operation as described in any of the first aspects above.

[0022] The technical solution of this application includes detecting dirt in a detection lens of a cleaning device: in response to the cleaning device being located in a target area, controlling the detection lens to emit a detection signal to a detection area corresponding to the target area; acquiring light reflection signals at each detection point in the detection area and determining light intensity data from the light reflection signals; and determining a first dirt evaluation index for the detection lens based on the light intensity data, wherein the first dirt evaluation index is used to characterize the degree of dirt on the detection lens.

[0023] Based on the technical solution of this application, by analyzing the light intensity data corresponding to the detection signal emitted by the detection lens in the target area, the degree of dirtiness of the detection lens can be automatically and accurately determined. After determining the degree of dirtiness of the detection lens, it is beneficial to understand the accuracy of the detection data collected by the detection lens, thereby guiding the cleaning equipment to perform corresponding actions, overcoming the defects of inaccurate detection data, and enabling the cleaning equipment to obtain accurate detection data. By analyzing the accurate detection data, the cleaning equipment can be guided to perform precise actions, thereby improving the cleaning quality of the cleaning equipment.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0026] Figure 1 A schematic flowchart of a method for detecting dirt in a detection lens according to an embodiment of this application is shown;

[0027] Figure 2 A detailed flowchart illustrating a process for determining a first dirt assessment index of the detection lens based on the light intensity data, according to an embodiment of this application, is shown.

[0028] Figure 3A detailed flowchart illustrating the determination of a reference feature value according to an embodiment of this application is shown;

[0029] Figure 4 A schematic diagram of a process for determining whether to send a prompt message to a user is shown according to an embodiment of this application;

[0030] Figure 5 A detailed flowchart illustrating a process for determining whether to send a prompt message to a user based on a first dirt assessment index and / or a second dirt assessment index, according to an embodiment of this application, is shown.

[0031] Figure 6 A block diagram of a dirt detection device for a detection lens according to an embodiment of this application is shown;

[0032] Figure 7 A schematic diagram of a cleaning device according to an embodiment of this application is shown. Detailed Implementation

[0033] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0034] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0035] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0036] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0037] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0038] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] It should be noted that the cleaning equipment mentioned in this application includes, but is not limited to, intelligent devices with cleaning functions such as sweeping machines, mopping machines, and combined sweeping and mopping machines.

[0041] It should also be noted that the detection lens mentioned in this application is installed in the cleaning equipment. The detection lens can emit light signal type detection signal. The detection lens can be a TOF sensor, i.e., a time-of-flight sensor, or a lidar, or other devices that detect based on emitted light signals. Specifically, this application does not limit it here.

[0042] Typically, cleaning equipment analyzes the detection data collected by the detection lens to build a cleaning map, navigate, and perform cleaning actions. However, since some parts of the detection lens are exposed to the air, these exposed parts are prone to accumulating pollutants such as hair, dust, and oil, which can easily cause varying degrees of contamination to the detection lens.

[0043] When a detection lens is contaminated, it affects the accuracy of the detection data it collects, and may even cause some data to be missing. For example, with Time-of-Flight (TOF) sensors, if the sensor is severely contaminated, its effective ranging range will be shortened, and the number of detection points it can detect will also decrease. Therefore, if cleaning equipment uses detection data collected by a severely contaminated TOF sensor, it will reduce the accuracy of the cleaning equipment's actions and the accuracy of the constructed environmental map.

[0044] Based on this, this application proposes a method for detecting dirt in a detection lens, which can accurately detect the degree of dirt in the detection lens and guide the cleaning equipment to perform corresponding actions, thereby overcoming the defect of inaccurate detection data.

[0045] The following detailed description of some embodiments of this application will be provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0046] See Figure 1 The diagram shows a flow chart of a dirt detection method for a detection lens according to an embodiment of this application, specifically including the following steps 110 to 130:

[0047] Step 110: In response to the cleaning device being located in the target area, control the detection lens to emit a detection signal to the detection area corresponding to the target area.

[0048] In some implementations, the target area can be an area with relatively abundant detection data. The target area can be determined by a pre-constructed environmental map of the area to be cleaned. For example, a target coordinate can be determined in the pre-constructed environmental map, and then the area within a preset range of the target coordinate can be used as the target area.

[0049] In some implementations, the target area can be determined during the process of constructing an environmental map of the area to be cleaned. For example, if a certain area is found to have relatively abundant detection data during the process of constructing an environmental map by the cleaning equipment, then that area can be used as the target area.

[0050] In some implementations, cleaning equipment can determine whether it is located in the target area by obtaining its own location information in real time.

[0051] In some implementations, step 110 can be performed during the cleaning device's current cleaning task. Therefore, if the cleaning device is located in the target area multiple times during the current cleaning task, the detection lens can be controlled to send detection signals to the detection area multiple times.

[0052] See also Figure 1 Step 120: Obtain the light reflection signal of each detection point in the detection area, and determine the light intensity data from the light reflection signal.

[0053] Typically, a detection lens includes a light emitting unit and a light receiving unit. The light emitting unit transmits the detection signal, and the light receiving unit receives the reflected light signal from the detection signal. Therefore, cleaning equipment can obtain the reflected light signal through the light receiving unit of the detection lens.

[0054] During the current cleaning task, the cleaning equipment may be located in the target area multiple times, and may adjust its posture by rotating when in the target area. Therefore, during the current cleaning task, the cleaning equipment may control the detection lens to emit detection signals to the detection area multiple times. After each emission of detection signal, each detection point in the detection area will reflect the emitted detection signal. Therefore, the light reflection signal mentioned in step 120 may include the light signal reflected by each detection point of the cleaning equipment to each emitted detection signal during the current cleaning task.

[0055] It is understandable that the orientation of the cleaning equipment may differ each time the detection lens emits a detection signal. Therefore, if the light signal reflected by each detection point from each emitted detection signal is used as the light reflection signal, the obtained light reflection signal will include the light signals reflected by each detection point under different fields of view of the cleaning equipment, which can increase the comprehensiveness and richness of the obtained light intensity data, thereby helping to improve the accuracy of subsequent determination of the degree of dirtiness of the detection lens.

[0056] In step 120, the light reflection signal includes several light signals. A specific implementation for determining light intensity data from the light reflection signal can be: determining the light intensity value from each light signal to obtain the light intensity data. Therefore, it can be understood that the light intensity data includes several light intensity values, and each light intensity value corresponds to a specific detection point.

[0057] See also Figure 1 Step 130: Based on the light intensity data, determine the first dirt evaluation index of the detection lens, which is used to characterize the degree of dirtiness of the detection lens.

[0058] In step 130, the specific implementation method for determining the first dirt evaluation index of the detection lens based on the light intensity data can be as follows: Figure 2 Perform the steps shown.

[0059] See Figure 2The diagram illustrates a detailed process for determining a first dirt index of the detection lens based on the light intensity data according to an embodiment of this application, specifically including the following steps 131 to 132:

[0060] Step 131: Based on the light intensity data, determine the target feature value of the detection lens in at least one evaluation dimension.

[0061] In this embodiment, the number of evaluation dimensions can be one or more, and this application does not limit the number of evaluation dimensions.

[0062] In determining the first contamination index of the detection lens, it can be determined from different evaluation dimensions. For different evaluation dimensions, the target feature value on the corresponding evaluation dimension can be determined. Thus, on one evaluation dimension, one target feature value can be determined, or multiple target feature values ​​can be determined. That is, the number of target feature values ​​can be equal to or different from the number of evaluation dimensions.

[0063] In step 131, based on the light intensity data, the target feature value of the detection lens in at least one evaluation dimension is determined to have at least two of the following implementation methods:

[0064] The first implementation method can be performed according to the following steps 131A:

[0065] Step 131A: According to the preset calculation rules, calculate each light intensity value in the light intensity data to obtain the target feature value of the detection lens in at least one evaluation dimension.

[0066] In some implementations, the preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation.

[0067] In this embodiment, the evaluation dimension is associated with the operation types included in the preset operation rules. A target feature value on one evaluation dimension is obtained through one operation rule. Furthermore, the number of operation rule types included in the preset operation rules is the same as the number of evaluation dimensions and the same as the number of target feature values.

[0068] For example, assuming the preset calculation rules are average light intensity calculation and maximum light intensity calculation, in this embodiment, by performing average light intensity calculation on each light intensity value in the light intensity data, we can obtain average light intensity 1, and by performing maximum light intensity calculation on each light intensity value in the light intensity data, we can obtain maximum light intensity 1. Therefore, in this embodiment, average light intensity 1 is the target feature value in one evaluation dimension, and maximum light intensity 1 is the target feature value in another evaluation dimension.

[0069] In the second implementation, the light intensity data includes at least one set of light intensity values, wherein the distance between each detection point corresponding to each set of light intensity values ​​and the target area satisfies a first preset condition. Furthermore, it can be executed according to the following step 131B:

[0070] Step 131B: According to the preset calculation rules, calculate each set of light intensity values ​​in the at least one set of light intensity values ​​to obtain the target feature value of the detection lens in at least one evaluation dimension.

[0071] In this embodiment, the light intensity data may include one set of light intensity values ​​or multiple sets of light intensity values. Specifically, this application does not limit the number of light intensity value sets.

[0072] In this embodiment, the first preset condition can be that the distance between each detection point corresponding to each set of light intensity values ​​and the target area is equal; or it can be that the distance between each detection point corresponding to each set of light intensity values ​​and the target area is within a set distance range.

[0073] It is understandable that if the first preset condition is that the distance between each detection point corresponding to each set of light intensity values ​​and the target area is equal, and the distance between each detection point corresponding to each set of light intensity values ​​and the target area is defined as the target distance, then the target distances corresponding to each set of light intensity values ​​included in the light intensity data are all different.

[0074] In this embodiment, the preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation. Of course, the preset calculation rules may also include other calculation rules, which are not limited herein.

[0075] Typically, the distances from each detection point within the detection area to the target area are not entirely equal, and the materials of the objects on which the detection points are located may also differ. For example, if the target area is located in a user's living room, the detection points in the detection area may be distributed on the television, the sofa, or the walls, etc.

[0076] Therefore, each set of light intensity values ​​can be determined based on the distance between each detection point and the target area. Specifically, one can first determine the target distance between the detection point and the target area corresponding to each light intensity value in the light intensity data, thus obtaining the correspondence between each light intensity value in the light intensity data and the target distance. Furthermore, based on this correspondence, each set of light intensity values ​​can be extracted from the light intensity data.

[0077] For example, suppose the light intensity data includes light intensity values ​​1-10, where the distance between the detection point corresponding to light intensity values ​​1-3 and the target area is 0.5m; the distance between the detection point corresponding to light intensity values ​​4-6 and the target area is 1m; the distance between the detection point corresponding to light intensity values ​​7-8 and the target area is 1.5m; and the distance between the detection point corresponding to light intensity values ​​9-10 and the target area is 2m. Then, light intensity values ​​1-3 can be considered as one set of light intensity values, and light intensity values ​​7-8 as another set. Under these conditions, the first preset condition is that the distance between each detection point corresponding to each set of light intensity values ​​and the target area is equal. Alternatively, light intensity values ​​1-6 can be considered as one set of light intensity values, and light intensity values ​​7-10 as another set. Under these conditions, the first preset condition is that the distance between each detection point corresponding to each set of light intensity values ​​and the target area is within a set distance range.

[0078] In step 131B, the evaluation dimensions can be understood from two perspectives.

[0079] From the first perspective, the evaluation dimension is associated with the types of operations included in the preset operation rules, as well as each set of light intensity values ​​included in the light intensity data. Through an operation rule, a set of light intensity values ​​is processed to obtain a target feature value on one evaluation dimension. Furthermore, if the number of operation rules included in the preset operation rules is defined as the first quantity, and the number of sets of at least one set of light intensity values ​​included in the light intensity data is defined as the second quantity, then the product of the first quantity and the second quantity is the same as the number of evaluation dimensions, and the number of evaluation dimensions is the same as the number of target feature values.

[0080] For example, assume that the light intensity data includes two sets of light intensity values. In the first set of light intensity values, the distance between the detection point corresponding to each light intensity value and the target area is 1m. In the second set of light intensity values, the distance between the detection point corresponding to each light intensity value and the target area is 2m. Also assume that the preset calculation rules include the calculation of the average light intensity value and the calculation of the maximum light intensity value.

[0081] Furthermore, by averaging the light intensity values ​​in the first group of light intensity values, we can obtain the average light intensity value 11, and by maximizing the light intensity values ​​in the first group of light intensity values, we can obtain the maximum light intensity value 11; by averaging the light intensity values ​​in the second group of light intensity values, we can obtain the average light intensity value 12, and by maximizing the light intensity values ​​in the two groups of light intensity values, we can obtain the maximum light intensity value 12.

[0082] Furthermore, the average light intensity 11 is the target feature value in the first evaluation dimension, the maximum light intensity 11 is the target feature value in the second evaluation dimension, the average light intensity 12 is the target feature value in the third evaluation dimension, and the maximum light intensity 12 is the target feature value in the fourth evaluation dimension.

[0083] From the second perspective, the evaluation dimension is only associated with the number of at least one set of light intensity values ​​included in the light intensity data. Under a set of light intensity values, the target feature value is obtained in one evaluation dimension, that is, the number of at least one set of light intensity values ​​is the same as the number of evaluation dimensions.

[0084] For example, assume that the light intensity data includes two sets of light intensity values. In the first set of light intensity values, the distance between the detection point corresponding to each light intensity value and the target area is 1m. In the second set of light intensity values, the distance between the detection point corresponding to each light intensity value and the target area is 2m. Also assume that the preset calculation rules include the calculation of the average light intensity value and the calculation of the maximum light intensity value.

[0085] Furthermore, by averaging the light intensity values ​​in the first group of light intensity values, we can obtain the average light intensity value 11, and by maximizing the light intensity values ​​in the first group of light intensity values, we can obtain the maximum light intensity value 11; by averaging the light intensity values ​​in the second group of light intensity values, we can obtain the average light intensity value 12, and by maximizing the light intensity values ​​in the two groups of light intensity values, we can obtain the maximum light intensity value 12.

[0086] Furthermore, the average light intensity 11 and the maximum light intensity 11 are target feature values ​​in one evaluation dimension, and the average light intensity 12 and the maximum light intensity 12 are target feature values ​​in another evaluation dimension.

[0087] In summary, based on the technical solution of this application, the target feature value in at least one evaluation dimension can be determined through light intensity data, that is, at least one target feature value can be determined, which is conducive to accurately determining the first dirt evaluation value of the detection lens.

[0088] See also Figure 2 Step 132: Compare the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

[0089] In one implementation, one or more target feature values ​​can be selected from the determined target feature values, and a first dirt evaluation index for the detection lens can be determined by comparing the one or more target feature values ​​with the corresponding reference feature values.

[0090] In another implementation, each of the determined target feature values ​​can be compared with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

[0091] In some implementations, the target feature value and the reference feature value correspond one-to-one. The evaluation dimension and data type of the target feature value are the same as the evaluation dimension and data type of the reference feature value corresponding to the target feature value.

[0092] For example, if the determined target feature value is the light intensity average value 11 mentioned above, then the reference feature value corresponding to the light intensity average value 11 is in the first evaluation dimension, and the reference feature value is also a light intensity average value data type.

[0093] In some implementations, the reference characteristic value can be determined by collecting light intensity data, provided that the detection lens of the cleaning equipment is uncontaminated or only slightly contaminated.

[0094] Specifically, there are at least two ways to determine the reference feature value.

[0095] In the first implementation method, reference feature values ​​corresponding to each target feature value are determined by using target experimental test data obtained through pre-testing.

[0096] Specifically, experimental tests can be conducted on different models of cleaning equipment to obtain experimental test data for each model. The detection lens of the cleaning equipment used in the experiment is an uncontaminated detection lens. Furthermore, by using the model of this cleaning equipment, experimental test data corresponding to this model can be obtained as the target experimental test data.

[0097] The experimental test for each model of cleaning equipment can be carried out by controlling the detection lens of the cleaning equipment to emit detection signals to the test area and acquiring the reference light reflection signals of each test point in the test area. Then, the reference light intensity data is determined from the reference light reflection signals, and the reference feature value in at least one evaluation dimension is determined based on the reference light intensity data. After that, each reference feature value can be used as the experimental test data of the corresponding model of cleaning equipment.

[0098] The second implementation method can be as follows: Figure 3 The steps shown are executed to obtain the desired result.

[0099] See Figure 3 The diagram illustrates a detailed process for determining a reference feature value according to an embodiment of this application, including the following steps 310 to 320:

[0100] Step 310: Obtain the light intensity data determined by the cleaning equipment during the execution of historical cleaning tasks, and use it as reference light intensity data.

[0101] It is understandable that the reference light intensity data includes light intensity data determined by the cleaning equipment during historical cleaning tasks and when it is located in the target area.

[0102] In one implementation, the historical cleaning task is the initial cleaning task of the cleaning equipment. The initial cleaning task can be the first cleaning task performed by the cleaning equipment after it has been purchased by the user; the initial cleaning task can also be the first cleaning task performed by the cleaning equipment after it has been reset.

[0103] It is understandable that when the cleaning equipment performs the initial cleaning task, the detection lens of the cleaning equipment is in a relatively clean state. Therefore, using the light intensity data during the initial cleaning task as reference light intensity data can improve the accuracy of the reference feature values ​​obtained subsequently, and thus improve the accuracy of determining the degree of dirtiness of the detection lens.

[0104] In another implementation, the historical cleaning tasks include an initial cleaning task and a set number of cleaning tasks performed after the initial cleaning task. The set number of cleaning tasks can be 7, 8, 9, etc., and this application does not limit the specific number of cleaning tasks.

[0105] For example, the historical cleaning tasks could be the first 10 cleaning tasks that the cleaning device will perform after being purchased by the user. Alternatively, the historical cleaning tasks could be the first 10 cleaning tasks that the cleaning device will perform after being reset.

[0106] In another implementation, the historical cleaning tasks include all cleaning tasks prior to the current cleaning task. That is, cleaning tasks that have already been completed by the cleaning equipment are considered historical cleaning tasks.

[0107] See also Figure 3 Step 320: Based on the reference light intensity data, determine the reference feature value corresponding to the target feature value.

[0108] In one implementation, referring to step 131 above, a reference feature value in at least one evaluation dimension can be determined based on the reference light intensity data, and then a reference feature value corresponding to the target feature value can be determined from the reference feature values ​​in at least one evaluation dimension.

[0109] For example, assuming that based on the reference light intensity data, the reference feature value 1 in the first evaluation dimension, the reference feature value 2 in the second evaluation dimension, the reference feature value 3 in the third evaluation dimension, and the reference feature value 4 in the third evaluation dimension are determined according to the first perspective mentioned above.

[0110] Furthermore, assuming that the average light intensity 11 in the first evaluation dimension is selected and compared with the corresponding reference feature value, then reference feature value 1 can be determined from reference feature values ​​1-4 as the reference feature value corresponding to the average light intensity 11.

[0111] In another implementation, a reference feature value corresponding to the target feature value can be directly determined based on reference light intensity data.

[0112] For example, if the average light intensity 11 in the first evaluation dimension is selected for comparison with the corresponding reference feature value, and the maximum light intensity 11 in the second evaluation dimension is selected for comparison with the corresponding reference feature value, then the average light intensity in the first evaluation dimension can be determined based on the reference light intensity data, as the reference feature value corresponding to the average light intensity 11; and the maximum light intensity in the second evaluation dimension can be determined based on the reference light intensity data, as the reference feature value corresponding to the maximum light intensity 11.

[0113] In summary, based on steps 310 and 320, the reference feature value corresponding to the target feature value can be accurately determined, which in turn helps to accurately determine the degree of dirtiness of the detection lens.

[0114] After determining the reference feature value, step 132 above can be performed to compare the target feature value with the corresponding reference feature value in order to determine the first dirt evaluation index of the detection lens.

[0115] The specific implementation of step 132 is not limited here. Specifically, each implementation should follow the principle that the larger the absolute value of the difference between the target feature value and the corresponding reference feature value, the more serious the degree of dirtiness of the detection lens.

[0116] For example, the specific implementation of step 132 may include the following two methods.

[0117] The first implementation method can be performed according to the following steps 1321A to 1322A:

[0118] Step 1321A: For each target feature value, calculate the absolute value of the target difference between each target feature value and the corresponding reference feature value.

[0119] Step 1322A: Calculate the ratio between the absolute value of each target difference and the corresponding reference feature value to obtain the first dirt evaluation index of the detection lens.

[0120] For example, the target feature values ​​to be compared include the average light intensity 11 in the first evaluation dimension, the maximum light intensity 11 in the second evaluation dimension, the average light intensity 12 in the third evaluation dimension, and the maximum light intensity 12 in the fourth evaluation dimension. Then, based on step 132, reference feature value 1 corresponding to the average light intensity 11, reference feature value 2 corresponding to the maximum light intensity 11, reference feature value 3 corresponding to the average light intensity 12, and reference feature value 4 corresponding to the maximum light intensity 12 can be obtained.

[0121] Furthermore, the difference between the average light intensity 11 and the reference feature value 1 is calculated to obtain the absolute value of the target difference 1; the difference between the maximum light intensity 11 and the reference feature value 2 is calculated to obtain the absolute value of the target difference 2; the difference between the average light intensity 12 and the reference feature value 3 is calculated to obtain the absolute value of the target difference 3; and the difference between the maximum light intensity 12 and the corresponding reference feature value 4 is calculated to obtain the absolute value of the target difference 4.

[0122] Furthermore, the ratio between the absolute value of the target difference 1 and the reference feature value 1 is calculated to obtain the first dirtiness evaluation index 1 in the first evaluation dimension; the ratio between the absolute value of the target difference 2 and the reference feature value 2 is calculated to obtain the first dirtiness evaluation index 2 in the second evaluation dimension; the ratio between the absolute value of the target difference 3 and the reference feature value 3 is calculated to obtain the first dirtiness evaluation index 3 in the third evaluation dimension; and the ratio between the absolute value of the target difference 4 and the reference feature value 4 is calculated to obtain the first dirtiness evaluation index 4 in the fourth evaluation dimension.

[0123] In this embodiment, the first dirtiness assessment index is positively correlated with the degree of dirtiness of the detection lens; that is, the higher the value of the first dirtiness assessment index, the more severe the dirtiness of the detection lens. Therefore, the degree of dirtiness of the detection lens can be determined based on the magnitude of the first dirtiness assessment index.

[0124] The second implementation method can be performed according to the following steps 1321B:

[0125] Step 1321B: Calculate the ratio between each of the target feature values ​​and the corresponding reference feature values ​​to obtain the first dirt evaluation index of the detection lens.

[0126] For example, the target feature values ​​to be compared include the average light intensity 11 in the first evaluation dimension. Through the above step 132, the reference feature value corresponding to the average light intensity 11 is obtained as reference feature value 1. Then, the ratio of the average light intensity 11 to the reference feature value 1 is calculated to obtain the first dirt evaluation index of the detection lens in the first evaluation dimension.

[0127] In this embodiment, the first dirt evaluation index is negatively correlated with the degree of dirtiness of the detection lens. That is, the smaller the value of the first dirt evaluation index, the more serious the dirtiness of the detection lens. Therefore, the degree of dirtiness of the detection lens can be understood based on the value of the first dirt evaluation index.

[0128] In this application, after obtaining the first dirt index of the detection lens, the first dirt index can be used to overcome the defect that the detection data collected by the detection lens is inaccurate.

[0129] This application does not limit the specific way of using it, but at least the following two methods are exemplary.

[0130] In the first implementation method, the detection data collected by the detection lens is corrected based on the first dirt evaluation index.

[0131] Understandably, if the first dirtiness index indicates that the detection lens is severely dirty, then the detection data collected by the detection lens can be significantly corrected to improve the accuracy of the detection data; if the first dirtiness index indicates that the detection lens is only slightly dirty, then the detection data collected by the detection lens can be slightly corrected to improve the accuracy of the detection data; if the first dirtiness index indicates that the detection lens is not dirty, then no correction needs to be made to the detection data collected by the detection lens.

[0132] The second implementation method can be as follows: Figure 4 Perform the steps shown.

[0133] See Figure 4 The diagram illustrates a flowchart of determining whether to send a prompt message to the user according to an embodiment of this application, specifically including the following steps 410 to 420:

[0134] Step 410: Obtain the dirt evaluation index of the detection lens when the cleaning equipment is located in other areas, as the second dirt evaluation index.

[0135] In this application, one or more predetermined areas can be defined in the area to be cleaned, and one of the predetermined areas can be designated as the target area, and the areas in the one or more predetermined areas other than the target area can be designated as other areas.

[0136] In this application, the cleaning device can determine one or more predetermined areas from a constructed environmental map, wherein each predetermined area corresponds to relatively abundant detection data. The cleaning device can also determine one or more predetermined areas from the area to be cleaned during the process of constructing the environmental map.

[0137] In this application, one or more predetermined areas can be updated based on the process data of each cleaning task. For example, suppose the cleaning equipment identifies predetermined area 1, predetermined area 2, and predetermined area 3 in the environmental map. However, during a cleaning task, if the cleaning equipment repeatedly fails to reach predetermined area 2, it indicates that predetermined area 2 may be occupied by other items or separated by a door. Therefore, predetermined area 2 can be removed from one or more predetermined areas.

[0138] In step 410, for other areas, the same technical solution as in steps 110 to 130 above can be used to determine the dirt evaluation index of the detection lens when the cleaning equipment is located in other areas.

[0139] See also Figure 4 Step 420: Based on the first dirt evaluation index and / or the second dirt evaluation index, determine whether to send a prompt message to the user, the prompt message being used to prompt the user to clean the detection lens.

[0140] In this embodiment, the degree of dirtiness of the detection lens can be determined based on the first dirtiness evaluation index and / or the second dirtiness evaluation index. If it is determined that the degree of dirtiness of the detection lens will affect the detection data collected by the detection lens, then a prompt message needs to be sent to the user in a timely manner so that the detection lens can be cleaned in a timely manner.

[0141] In this embodiment, the method of sending a prompt message to the user may be to send a dirt cleaning prompt message to the user's client APP, or to send an SMS to the user's communication device, or to provide a voice prompt through the speaker built into the cleaning device, or other methods. Specifically, this application does not limit the specific methods.

[0142] In this embodiment, the prompt information sent to the user may include one or more of the following: the location information of the detection lens, the degree of dirt on the detection lens, the time when the detection lens was last cleaned, the cleaning method that can be used to clean the detection lens, and the operations to be performed after the detection lens is cleaned.

[0143] In this embodiment, the specific implementation method for determining whether to send a prompt message to the user based on the first dirt evaluation index and / or the second dirt evaluation index can be set according to the actual situation. This application does not limit it here. Four optional implementation methods are shown below.

[0144] In the first implementation, a target average of the first dirtiness evaluation index and the second dirtiness evaluation index is calculated. If the target average meets a third preset condition, a prompt message is sent to the user.

[0145] The third preset condition can be set according to the actual situation, and this application does not limit it. For example, assuming that the dirt evaluation index of the detection lens is positively correlated with the degree of dirtiness of the detection lens, then the third preset condition can be that the target average value is greater than the first preset value.

[0146] Understandably, if the average value of the target is greater than the first set value, it means that the degree of dirtiness of the detection lens will affect the accuracy of the detection data collected by the detection lens, so it is necessary to send a prompt message to the user.

[0147] In the second implementation, the maximum value of the dirt evaluation index is selected from the first dirt evaluation index and the second dirt evaluation index, and is taken as the maximum dirt evaluation index. If the maximum dirt evaluation index meets the fourth preset condition, a prompt message is sent to the user.

[0148] The fourth preset condition can be set according to the actual situation, and this application does not limit it. For example, assuming that the dirt evaluation index of the detection lens is positively correlated with the degree of dirtiness of the detection lens, then the fourth preset condition can be that the maximum value of the dirt evaluation index is greater than the second preset value.

[0149] Understandably, if the maximum value of the dirtiness evaluation index is greater than the second set value, it means that the degree of dirtiness of the detection lens will affect the accuracy of the detection data collected by the detection lens, so it is necessary to send a prompt message to the user.

[0150] In the third implementation, the number of first dirt assessment indices is greater than 1, and the number of second dirt assessment indices is greater than 1. The average value of each first dirt assessment index is calculated as the first average value, and the average value of each second dirt assessment index is calculated as the second average value. If the first average value and the second average value meet the fifth preset condition, a prompt message is sent to the user.

[0151] The fifth preset condition can be set according to the actual situation, and this application does not limit it. For example, assuming that the dirt evaluation index of the detection lens is positively correlated with the degree of dirt of the detection lens, the fifth preset condition can be that both the first average value and the second average value are greater than the third preset value; or it can be that the first average value is greater than the second preset value or the second average value is greater than the third preset value.

[0152] The fourth implementation method can be as follows: Figure 5 Perform the steps shown.

[0153] See Figure 5 The diagram illustrates a detailed process for determining whether to send a notification message to a user based on the first dirt assessment index and / or the second dirt assessment index, according to an embodiment of this application. Specifically, it includes the following steps 421 to 422:

[0154] Step 421: Compare the first dirt evaluation index and / or the second dirt evaluation index with a preset threshold to obtain the comparison result.

[0155] In this embodiment, the comparison result can be obtained by comparing the first dirt evaluation index with a preset threshold; or by comparing the second dirt evaluation index with a preset threshold; or by comparing the first dirt evaluation index and the second dirt evaluation index with a preset threshold.

[0156] In some implementations, the setting method of the preset threshold is related to the type of the first dirt evaluation index. If the first dirt evaluation index and the second dirt evaluation index are positively correlated with the degree of dirtiness of the detection lens, then the preset threshold can be set to a smaller value, such as 0.1, 0.2, etc. If the first dirt evaluation index and the second dirt evaluation index are negatively correlated with the degree of dirtiness of the detection lens, then the preset threshold can be set to a larger value, such as 0.8, 0.9, etc.

[0157] In some implementations, the preset threshold can be adjusted based on the usage time of the cleaning equipment. Because equipment inherently experiences degradation, the light decay of the detection lens increases with prolonged use. Therefore, the preset threshold can be adjusted based on the usage time of the cleaning equipment. For example, if the first and second dirt assessment indices are positively correlated with the degree of dirt on the detection lens, then the preset threshold can be set to 0.2 in the first year of use and adjusted to 0.3 in the second year.

[0158] In this embodiment, since the number of first dirt evaluation indices is greater than or equal to 1, if the first dirt evaluation index is greater than 1, each first dirt evaluation index should be compared with a preset threshold. Similarly, since the number of second dirt evaluation indices is also greater than or equal to 1, if the second dirt evaluation index is greater than 1, each second dirt evaluation index should be compared with a preset threshold.

[0159] In some implementations, the comparison result may include the target difference between a first dirt assessment index and / or a second dirt assessment index and a preset threshold.

[0160] See also Figure 5 Step 422: If the comparison result meets the second preset condition, a prompt message is sent to the user.

[0161] In this embodiment, the second preset condition can be set according to the actual situation. Specifically, this application does not limit the specific setting method of the second preset condition. Several possible setting methods for the second preset condition are illustrated below. It should be noted that the following examples all assume a positive correlation between the dirt evaluation index and the degree of dirt on the detection lens.

[0162] The first method has a second preset condition that the difference between each target is greater than 0.

[0163] For example, assume that the target feature values ​​determined in the target area include: average light intensity 11 and maximum light intensity 11, and the target feature values ​​determined in other areas include: average light intensity 13 and maximum light intensity 13. Calculations are performed using these values ​​and their corresponding reference feature values ​​to obtain the first dirt assessment index 1 and the first dirt assessment index 2, as well as the second dirt assessment index 1 and the second dirt assessment index 2.

[0164] Furthermore, if the first dirt assessment index 1 is greater than the preset threshold, and the first dirt assessment index 2 is greater than the preset threshold, and the second dirt assessment index 1 is greater than the preset threshold, and the second dirt assessment index 2 is greater than the preset threshold, then a prompt message is sent to the user.

[0165] The second approach has a second preset condition: all target differences on any evaluation dimension are greater than 0.

[0166] For example, suppose the target feature values ​​determined in the target area include: an average light intensity of 11 in the first evaluation dimension and a maximum light intensity of 11 in the second evaluation dimension; suppose the target feature values ​​determined in other areas include: an average light intensity of 13 in the first evaluation dimension and a maximum light intensity of 13 in the second evaluation dimension. By calculating with their respective corresponding reference feature values, a first dirtiness evaluation index 1 in the first evaluation dimension, a first dirtiness evaluation index 2 in the second evaluation dimension, a second dirtiness evaluation index 1 in the first evaluation dimension, and a second dirtiness evaluation index 2 in the second evaluation dimension are obtained.

[0167] Furthermore, if the first dirtiness evaluation index 1 in the first evaluation dimension is greater than a preset threshold, and the second dirtiness evaluation index 2 in the first evaluation dimension is greater than a preset threshold, then a prompt message is sent to the user; or, if the first dirtiness evaluation index 2 in the second evaluation dimension is greater than a preset threshold, and the second dirtiness evaluation index 2 in the second evaluation dimension is greater than a preset threshold, then a prompt message is sent to the user.

[0168] The third method has a second preset condition: in any dimension, the target difference exceeding the preset ratio is greater than 0.

[0169] For example, assuming the preset ratio in the second preset condition is 0.5, and assuming the target feature values ​​determined in the target area include: the average light intensity 11 in the first evaluation dimension and the maximum light intensity 11 in the second evaluation dimension; assuming the target feature values ​​determined in other areas include: the average light intensity 13 in the first evaluation dimension, the maximum light intensity 13 in the second evaluation dimension, the average light intensity 14 in the first evaluation dimension, and the maximum light intensity 14 in the second evaluation dimension. After calculation with their respective corresponding reference feature values, the following are obtained: the first dirt evaluation index 1 in the first evaluation dimension, the first dirt evaluation index 2 in the second evaluation dimension, the second dirt evaluation index 1 in the first evaluation dimension, the second dirt evaluation index 2 in the second evaluation dimension, the second dirt evaluation index 3 in the first evaluation dimension, and the second dirt evaluation index 4 in the second evaluation dimension.

[0170] Furthermore, if it is determined that the first dirtiness evaluation index 1 in the first evaluation dimension is greater than the preset threshold, and the second dirtiness evaluation index 1 in the first evaluation dimension is greater than the preset threshold, then a prompt message is sent to the user.

[0171] In summary, the second preset condition can be set according to the evaluation dimension and the number of target differences, and this application will not provide examples of each.

[0172] In this embodiment, if the comparison result meets the second preset condition, it indicates that the detection lens is seriously dirty. If the user is not prompted to clean it, it may cause serious problems, such as inaccurate navigation or the cleaning device executing an incorrect obstacle avoidance strategy.

[0173] In this application, if the comparison result does not meet the second preset condition, the following step 422A can also be performed:

[0174] Step 422A: If the comparison result does not meet the second preset condition, then based on the light intensity data, update the reference feature value corresponding to the target feature value.

[0175] It should be noted that if the comparison results do not meet the second preset condition, it means that the detection lens is not dirty, or the degree of dirt is very slight and will not affect the cleaning equipment's operation. Therefore, no prompt message needs to be sent to the user.

[0176] In one implementation, the reference feature value corresponding to the target feature value is determined by acquiring reference light intensity data. Therefore, if the comparison result does not meet the second preset condition, the light intensity data determined by the cleaning device during the current cleaning task can be combined with the light intensity data determined by the cleaning device during the historical cleaning task. Then, based on the combined light intensity data, statistics and calculations can be performed to determine a new reference feature value corresponding to the target feature value, so as to prepare for determining the dirt evaluation index of the detection lens when the cleaning device performs the next cleaning task.

[0177] Understandably, if the comparison result does not meet the second preset condition, and if step 422A is not executed, then when the cleaning equipment performs the cleaning task again, it will continue to use the reference characteristic value determined during the current cleaning task.

[0178] In summary, updating the reference feature value when the comparison result does not meet the second preset condition can ensure that the reference feature value is consistent with the current state of the detection lens in real time, thereby improving the accuracy of the determined first dirt evaluation index.

[0179] In this application, after completing step 422 above, steps 423 to 424 may be performed as follows:

[0180] Step 423: In response to receiving confirmation information that the detection lens has been cleaned, control the cleaning device to re-determine the dirt evaluation index of the detection lens and obtain a new dirt evaluation index.

[0181] Step 424: If the new dirt evaluation index is within the preset dirt evaluation index range, then the dirt cleaning of the detection lens is completed.

[0182] In some implementations, after cleaning the probe lens, the user can confirm the cleaning is complete in the client APP corresponding to the cleaning device, thereby sending a confirmation message to the cleaning device that the probe lens has been cleaned; the user can also perform touch operations on the cleaning device so that the cleaning device receives the confirmation message that the probe lens has been cleaned.

[0183] In some implementations, if the cleaning device receives the confirmation information, it can control the cleaning device to re-execute steps 110 to 130 during the next cleaning task, thereby obtaining a new dirt evaluation index for the detection lens.

[0184] In some implementations, the preset dirt evaluation index range can be set based on a preset threshold. For example, the preset dirt evaluation index range can be obtained by fluctuating within a certain range above and below the preset threshold. For instance, if the preset threshold is 0.9, then the preset dirt evaluation index range can be set to 0.8-1.2.

[0185] It is understandable that, because the detection lens itself has a certain light decay, the new dirt evaluation index determined after cleaning the detection lens may still not meet the second preset condition. Therefore, the comparison rules can be adjusted accordingly to avoid the cleaning equipment making a judgment error.

[0186] In some implementations, if the new dirt assessment index is not within the preset dirt assessment index range, it is determined that the dirt cleaning of the detection lens has not been completed, and a prompt message is sent to the user again.

[0187] In summary, after receiving confirmation that the cleaning device has completed cleaning the detection lens, the cleaning device can determine whether the user has actually completed cleaning the detection lens by re-evaluating the dirt assessment index. If it is determined that the user has not actually completed cleaning the detection lens, the cleaning device can continue to remind the user to prevent the cleaning device from causing more serious problems.

[0188] In this application, after completing step 424 above, step 425 can be performed as follows:

[0189] Step 425: Delete the data recorded by the cleaning equipment that is associated with the reference feature value.

[0190] It should be noted that the data associated with the reference feature value includes reference light intensity data, etc. Deleting the data associated with the reference feature value indicates that the next cleaning task performed by the cleaning equipment is the initial cleaning task, which allows for the determination of new reference light intensity data, thereby preparing for future determination of the degree of dirt on the detection lens.

[0191] In this application, if step 425 is not executed after step 424 is completed, it means that the cleaning equipment can continue to use the reference light intensity data used in the current cleaning task during the next cleaning task.

[0192] In summary, after the cleaning equipment has completed the cleaning of the probe lens, it is possible to choose whether or not to delete the data associated with the reference feature value. This application does not limit the specific implementation method, and those skilled in the art can make the choice according to the actual situation.

[0193] In some embodiments of this application, the technical solution for detecting dirt in a detection lens of a cleaning device includes: in response to the cleaning device being located in a target area, controlling the detection lens to emit a detection signal to a detection area corresponding to the target area; acquiring light reflection signals at each detection point within the detection area and determining light intensity data from the light reflection signals; and determining a first dirt evaluation index for the detection lens based on the light intensity data, wherein the first dirt evaluation index is used to characterize the degree of dirt on the detection lens.

[0194] Based on the technical solution of this application, by analyzing the light intensity data corresponding to the detection signal emitted by the detection lens in the target area, the degree of dirtiness of the detection lens can be automatically and accurately determined. After determining the degree of dirtiness of the detection lens, it is beneficial to understand the accuracy of the detection data collected by the detection lens, thereby guiding the cleaning equipment to perform corresponding actions to overcome the defects of inaccurate detection data. For example, when it is determined that the degree of dirtiness of the detection lens is relatively serious, a prompt message can be sent to the user in a timely manner, so that the user can clean the detection lens in time. Thus, a clean detection lens enables the cleaning equipment to obtain accurate detection data. By analyzing the accurate detection data, the cleaning equipment can be guided to perform precise actions, thereby improving the cleaning quality of the cleaning equipment.

[0195] Based on the same inventive concept, embodiments of this application provide a dirt detection device for a detection lens, which can be used to perform the dirt detection method for a detection lens described in the above embodiments of this application. For details not disclosed in the embodiments of this application, please refer to the embodiments of the dirt detection method for a detection lens described above.

[0196] See Figure 6The diagram shows a block diagram of a dirt detection device for a detection lens according to an embodiment of this application.

[0197] like Figure 6 As shown, a dirt detection device 600 for a detection lens according to an embodiment of this application is provided. The detection lens is installed on a cleaning device. The device 600 includes: a control unit 601, an acquisition unit 602, and a determination unit 603.

[0198] The control unit 601 is configured to control the detection lens to emit a detection signal to the detection area corresponding to the target area in response to the cleaning device being located in the target area; the acquisition unit 602 is configured to acquire the light reflection signal of each detection point in the detection area and determine the light intensity data from the light reflection signal; the determination unit 603 is configured to determine a first dirt evaluation index of the detection lens based on the light intensity data, wherein the first dirt evaluation index is used to characterize the degree of dirtiness of the detection lens.

[0199] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: determine the target feature value of the detection lens in at least one evaluation dimension based on the light intensity data; and compare the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

[0200] In some embodiments of this application, based on the aforementioned scheme, the light intensity data includes at least one set of light intensity values, wherein the distance between each detection point corresponding to each set of light intensity values ​​and the target area satisfies a first preset condition.

[0201] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: perform calculations on each of the at least one set of light intensity values ​​according to preset calculation rules to obtain the target feature value of the detection lens in at least one evaluation dimension.

[0202] In some embodiments of this application, based on the foregoing scheme, the preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation.

[0203] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: acquire light intensity data determined by the cleaning device during the execution of historical cleaning tasks, as reference light intensity data; and determine a reference feature value corresponding to the target feature value based on the reference light intensity data.

[0204] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: calculate the absolute value of the target difference between each target feature value and the corresponding reference feature value for each target feature value; calculate the ratio between each absolute value of the target difference and the corresponding reference feature value to obtain the first dirt evaluation index of the detection lens.

[0205] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: calculate the ratio between each of the target feature values ​​and the corresponding reference feature values ​​to obtain the first dirt evaluation index of the detection lens.

[0206] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: obtain the dirt evaluation index of the detection lens when the cleaning device is located in other areas, as a second dirt evaluation index; and determine whether to send a prompt message to the user based on the first dirt evaluation index and / or the second dirt evaluation index, the prompt message being used to prompt the user to clean the dirt from the detection lens.

[0207] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: compare the first dirt evaluation index and / or the second dirt evaluation index with a preset threshold to obtain a comparison result; if the comparison result meets a second preset condition, then send a prompt message to the user.

[0208] In some embodiments of this application, based on the foregoing scheme, the determining unit 603 is further configured to: if the comparison result does not meet the second preset condition, update the reference feature value corresponding to the target feature value based on the light intensity data.

[0209] In some embodiments of this application, based on the foregoing scheme, after sending a prompt message to the user, the determining unit 603 is further configured to: in response to receiving confirmation information that the cleaning of the detection lens has been completed, control the cleaning device to re-determine the dirt evaluation index of the detection lens to obtain a new dirt evaluation index; if the new dirt evaluation index is within the preset dirt evaluation index range, then determine that the cleaning of the detection lens has been completed.

[0210] In some embodiments of this application, based on the foregoing scheme, after determining that the cleaning of the detection lens is completed, the determining unit 603 is further configured to: delete the data recorded by the cleaning device that is associated with the reference feature value.

[0211] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described above.

[0212] Based on the same inventive concept, embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described above.

[0213] Based on the same inventive concept, this application also provides a cleaning device.

[0214] See Figure 7 The diagram shows a structural schematic of a cleaning device according to an embodiment of the present application. The cleaning device is equipped with a detection lens and includes one or more memories 704, one or more processors 702, and at least one computer program (computer program instructions) stored in the memory 704 and executable on the processor 702. When the processor 702 executes the computer program, it implements the method described above.

[0215] Among them, Figure 7 In this document, a bus architecture (represented by bus 700) is used. Bus 700 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 702 and memory represented by memory 704. Bus 700 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 705 provides an interface between bus 700 and receiver 701 and transmitter 703. Receiver 701 and transmitter 703 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 702 is responsible for managing bus 700 and general processing, while memory 704 can be used to store data used by processor 702 during operation.

[0216] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0217] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0218] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0219] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0220] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting dirt on a detection lens, characterized in that, The detection lens is installed on the cleaning equipment, and the method includes: In response to the cleaning equipment being located in the target area, the detection lens is controlled to emit a detection signal toward the detection area corresponding to the target area; Acquire the light reflection signals of each detection point within the detection area, and determine the light intensity data from the light reflection signals; Based on the light intensity data, a first dirtiness evaluation index is determined for the detection lens, which is used to characterize the degree of dirtiness of the detection lens. The cleaning device determines whether it is located in the target area by acquiring its own location information in real time; the target area is an area with relatively abundant detection data.

2. The method according to claim 1, characterized in that, The determination of the first dirt assessment index of the detection lens based on the light intensity data includes: Based on the light intensity data, the target feature value of the detection lens in at least one evaluation dimension is determined; The target feature value is compared with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

3. The method according to claim 2, characterized in that, The light intensity data includes at least one set of light intensity values, wherein the distance between each detection point corresponding to each set of light intensity values ​​and the target area satisfies a first preset condition.

4. The method according to claim 3, characterized in that, The step of determining the target feature value of the detection lens in at least one evaluation dimension based on the light intensity data includes: According to the preset calculation rules, each of the at least one set of light intensity values ​​is calculated to obtain the target feature value of the detection lens in at least one evaluation dimension.

5. The method according to claim 4, characterized in that, The preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation.

6. The method according to claim 2, characterized in that, The method further includes: The light intensity data determined by the cleaning equipment during the execution of historical cleaning tasks is obtained as reference light intensity data; Based on the reference light intensity data, a reference feature value corresponding to the target feature value is determined.

7. The method according to claim 2, characterized in that, The step of comparing the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens includes: For each target feature value, calculate the absolute value of the target difference between each target feature value and the corresponding reference feature value; The ratio between the absolute value of each target difference and the corresponding reference feature value is calculated to obtain the first dirt evaluation index of the detection lens.

8. The method according to claim 2, characterized in that, The step of comparing the target feature value with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens includes: The ratio between each target feature value and its corresponding reference feature value is calculated to obtain the first dirt evaluation index of the detection lens.

9. The method according to claim 2, characterized in that, The method further includes: The dirt assessment index of the detection lens, determined when the cleaning equipment is located in other areas, is used as the second dirt assessment index. Based on the first dirt assessment index and / or the second dirt assessment index, it is determined whether to send a prompt message to the user, the prompt message being used to remind the user to clean the detection lens.

10. The method according to claim 9, characterized in that, The step of determining whether to send a prompt message to the user based on the first dirt assessment index and / or the second dirt assessment index includes: The first dirt assessment index and / or the second dirt assessment index are compared with a preset threshold to obtain the comparison result; If the comparison result meets the second preset condition, a prompt message is sent to the user.

11. The method according to claim 10, characterized in that, The method further includes: If the comparison result does not meet the second preset condition, then based on the light intensity data, the reference feature value corresponding to the target feature value is updated.

12. The method according to claim 11, characterized in that, After sending the notification message to the user, the method further includes: In response to receiving confirmation information that the detection lens has been cleaned, the cleaning device is controlled to re-determine the dirt evaluation index of the detection lens and obtain a new dirt evaluation index. If the new dirt assessment index is within the preset dirt assessment index range, then the dirt cleaning of the detection lens is determined to be complete.

13. The method according to claim 12, characterized in that, After confirming that the cleaning of the probe lens is complete, the method further includes: Delete the data recorded by the cleaning equipment that is associated with the reference feature value.

14. A dirt detection device for a detection lens, characterized in that, The detection lens is mounted on the cleaning equipment, and the device includes: A control unit is configured to control the detection lens to emit a detection signal toward a detection area corresponding to the target area in response to the cleaning equipment being located in the target area; The acquisition unit is used to acquire the light reflection signals of each detection point within the detection area and determine the light intensity data from the light reflection signals; The determining unit is configured to determine a first dirtiness evaluation index of the detection lens based on the light intensity data, wherein the first dirtiness evaluation index is used to characterize the degree of dirtiness of the detection lens; The cleaning device determines whether it is located in the target area by acquiring its own location information in real time; the target area is an area with relatively abundant detection data.

15. The apparatus according to claim 14, characterized in that, The determining unit is further configured to: Based on the light intensity data, the target feature value of the detection lens in at least one evaluation dimension is determined; The target feature value is compared with the corresponding reference feature value to determine the first dirt evaluation index of the detection lens.

16. The apparatus according to claim 15, characterized in that, The light intensity data includes at least one set of light intensity values, wherein the distance between each detection point corresponding to each set of light intensity values ​​and the target area satisfies a first preset condition.

17. The apparatus according to claim 16, characterized in that, The determining unit is further configured to: According to the preset calculation rules, each of the at least one set of light intensity values ​​is calculated to obtain the target feature value of the detection lens in at least one evaluation dimension.

18. The apparatus according to claim 17, characterized in that, The preset calculation rules include at least one of the following: average light intensity calculation, maximum light intensity calculation, minimum light intensity calculation, and light intensity variance calculation.

19. The apparatus according to claim 15, characterized in that, The determining unit is further configured to: The light intensity data determined by the cleaning equipment during the execution of historical cleaning tasks is obtained as reference light intensity data; Based on the reference light intensity data, a reference feature value corresponding to the target feature value is determined.

20. The apparatus according to claim 15, characterized in that, The determining unit is further configured to: For each target feature value, calculate the absolute value of the target difference between each target feature value and the corresponding reference feature value; The ratio between the absolute value of each target difference and the corresponding reference feature value is calculated to obtain the first dirt evaluation index of the detection lens.

21. The apparatus according to claim 15, characterized in that, The determining unit is further configured to: The ratio between each target feature value and its corresponding reference feature value is calculated to obtain the first dirt evaluation index of the detection lens.

22. The apparatus according to claim 15, characterized in that, The determining unit is further configured to: The dirt assessment index of the detection lens, determined when the cleaning equipment is located in other areas, is used as the second dirt assessment index. Based on the first dirt assessment index and / or the second dirt assessment index, it is determined whether to send a prompt message to the user, the prompt message being used to remind the user to clean the detection lens.

23. The apparatus according to claim 22, characterized in that, The determining unit is further configured to: The first dirt assessment index and / or the second dirt assessment index are compared with a preset threshold to obtain the comparison result; If the comparison result meets the second preset condition, a prompt message is sent to the user.

24. The apparatus according to claim 23, characterized in that, The determining unit is further configured to: If the comparison result does not meet the second preset condition, then based on the light intensity data, the reference feature value corresponding to the target feature value is updated.

25. The apparatus according to claim 24, characterized in that, After sending the prompt message to the user, the determining unit is further configured to: In response to receiving confirmation information that the detection lens has been cleaned, the cleaning device is controlled to re-determine the dirt evaluation index of the detection lens and obtain a new dirt evaluation index. If the new dirt assessment index is within the preset dirt assessment index range, then the dirt cleaning of the detection lens is determined to be complete.

26. The apparatus according to claim 25, characterized in that, After confirming that the cleaning of the probe lens is complete, the determining unit is further configured to: Delete the data recorded by the cleaning equipment that is associated with the reference feature value.

27. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 13.

28. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the method as claimed in any one of claims 1 to 13.

29. A cleaning device, characterized in that, The cleaning device is equipped with a detection lens and includes one or more processors and one or more memories, wherein the one or more memories store at least one piece of program code, which is loaded and executed by the one or more processors to implement the method as claimed in any one of claims 1 to 13.

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